Design pedagogy material for Machine Learning in Dutch high schools

Motivation

Skills in artificial intelligence and data science are becoming more relevant in today’s labour market¹. Furthermore, the advances in artificial intelligence are reaching an audience beyond experts in the field². Thus, it is becoming more important to develop social appropriation strategies for the research in artificial intelligence. This project aims to develop tools to teach machine learning concepts with hands-on approach focusing on teenagers above 16 years old. The resulting tools are expected to catch and maintain the motivation of the students while teaching abstract concepts in an easy-to-follow way.

Tasks and Timeline

Stage 1:

• Define the problem of pedagogy of machine learning in high schools;
• Delimitate the solution concept;
• Review current tools utilized in machine learning teaching for universities and high schools;
• Brainstorm potential solutions for the problem.

Stage 2:

• Plan the experiments and test for the expected solution;
• Propose a new tool for machine learning education with an emphasis on high school;
• Design a proof-of-concept for potential solutions via minimum viable product concept;
• Analyze the proposed proof-of-concept.

Stage 3:

• Evaluate the proof-of-concept with the proposed experiments and tests;
• Benchmark the proof-of-concept against other solutions in the literature.

Stage 4:

• Document the problem description and solution-design methodology;
• Show the results to reviewers and non-technical audiences.

Expected Outcomes

Mandatory Products

• Educational material for machine learning in high schools;
• Software and hardware with documentation associated with the project;
• Technical report with problem description, proposed solutions, experimental results, and project conclusions by following the University guidelines;
• A public dissertation following the University guidelines.

Optional Products

• Summary paper from the technical report suitable for conferences or journals;
• 3-minute elevator pitch video of the project;
• Blog post or video explaining the problem and proposed solution for a general audience.

Bibliography

• R. M. Martins and C. Gresse Von Wangenheim, “Findings on Teaching Machine Learning in High School: A Ten - Year Systematic Literature Review,” Informatics in Education, Sep. 2022, doi: 10.15388/infedu.2023.18.